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Predicting Financial Distress in Acute Care Hospitals
James R Langabeer1, Karima H Lalani2, Tiffany Champagne-Langabeer3
1a School of Biomedical Informatics , The University of Texas Health Science Center , Houston , Texas , USA.
Abstract:
Hospitals continue to face financial pressures from healthcare reform and heightened competition. In this study, our objective was to quantify the financial distress in acute care hospitals in Texas, applying multivariate logistic regression in a four-year longitudinal analysis. Of the 310 acute care hospitals, 50 (16.1%) were in financial distress in the most recent year, up considerably year over year. Distressed hospitals had fewer beds, lower patient acuity, and less outpatient revenues than those in good financial condition. Administrators should identify business turnaround strategies for combating distress to avoid potential closure.
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However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.